Overview

Dataset statistics

Number of variables31
Number of observations656
Missing cells0
Missing cells (%)0.0%
Duplicate rows18
Duplicate rows (%)2.7%
Total size in memory159.0 KiB
Average record size in memory248.2 B

Variable types

Numeric8
Categorical23

Warnings

Dataset has 18 (2.7%) duplicate rows Duplicates
STDs is highly correlated with STDs (number) and 1 other fieldsHigh correlation
STDs (number) is highly correlated with STDs and 1 other fieldsHigh correlation
STDs:condylomatosis is highly correlated with STDs:vulvo-perineal condylomatosisHigh correlation
STDs:vulvo-perineal condylomatosis is highly correlated with STDs:condylomatosisHigh correlation
STDs: Number of diagnosis is highly correlated with STDs and 1 other fieldsHigh correlation
STDs (number) is highly correlated with STDs:vulvo-perineal condylomatosis and 2 other fieldsHigh correlation
STDs:vulvo-perineal condylomatosis is highly correlated with STDs (number) and 1 other fieldsHigh correlation
STDs is highly correlated with STDs (number) and 1 other fieldsHigh correlation
STDs:condylomatosis is highly correlated with STDs (number) and 1 other fieldsHigh correlation
STDs: Number of diagnosis is highly correlated with STDsHigh correlation

Reproduction

Analysis started2021-03-10 21:48:51.995423
Analysis finished2021-03-10 21:48:59.299680
Duration7.3 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Age
Real number (ℝ)

Distinct42
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.299773297 × 1016
Minimum-1.531372742
Maximum6.135380072
Zeros0
Zeros (%)0.0%
Memory size5.2 KiB
2021-03-10T22:48:59.362543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.531372742
5-th percentile-1.295472655
Q1-0.8236724823
median-0.1159722225
Q30.7096780806
95-th percentile1.653278427
Maximum6.135380072
Range7.666752814
Interquartile range (IQR)1.533350563

Descriptive statistics

Standard deviation1.000763068
Coefficient of variation (CV)-7.699520138 × 1015
Kurtosis3.519930022
Mean-1.299773297 × 1016
Median Absolute Deviation (MAD)0.7077002598
Skewness1.2408354
Sum-8.526512829 × 1014
Variance1.001526718
MonotocityNot monotonic
2021-03-10T22:48:59.441846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
-1.05957256941
 
6.2%
-0.70572243937
 
5.6%
-0.823672482334
 
5.2%
-0.469822352434
 
5.2%
-0.941622525633
 
5.0%
-0.351872309131
 
4.7%
0.119927864130
 
4.6%
-0.233922265830
 
4.6%
-0.115972222528
 
4.3%
0.00197782084826
 
4.0%
Other values (32)332
50.6%
ValueCountFrequency (%)
-1.5313727424
 
0.6%
-1.41342269915
 
2.3%
-1.29547265517
2.6%
-1.17752261226
4.0%
-1.05957256941
6.2%
ValueCountFrequency (%)
6.1353800721
0.2%
5.0738296832
0.3%
3.7763792061
0.2%
2.9507289032
0.3%
2.832778861
0.2%

Number of sexual partners
Real number (ℝ)

Distinct12
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.707861036 × 1018
Minimum-0.8793010688
Maximum14.69487946
Zeros0
Zeros (%)0.0%
Memory size5.2 KiB
2021-03-10T22:48:59.511574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8793010688
5-th percentile-0.8793010688
Q1-0.3024795677
median-0.3024795677
Q30.2743419335
95-th percentile1.427984936
Maximum14.69487946
Range15.57418053
Interquartile range (IQR)0.5768215011

Descriptive statistics

Standard deviation1.000763068
Coefficient of variation (CV)-3.695769666 × 1017
Kurtosis74.96758173
Mean-2.707861036 × 1018
Median Absolute Deviation (MAD)0.5768215011
Skewness6.011230304
Sum-1.776356839 × 1015
Variance1.001526718
MonotocityNot monotonic
2021-03-10T22:48:59.670164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
-0.3024795677218
33.2%
0.2743419335168
25.6%
-0.8793010688161
24.5%
0.851163434657
 
8.7%
1.42798493634
 
5.2%
2.0048064377
 
1.1%
3.1584494394
 
0.6%
2.5816279383
 
0.5%
3.735270941
 
0.2%
4.3120924421
 
0.2%
Other values (2)2
 
0.3%
ValueCountFrequency (%)
-0.8793010688161
24.5%
-0.3024795677218
33.2%
0.2743419335168
25.6%
0.851163434657
 
8.7%
1.42798493634
 
5.2%
ValueCountFrequency (%)
14.694879461
 
0.2%
7.1961999471
 
0.2%
4.3120924421
 
0.2%
3.735270941
 
0.2%
3.1584494394
0.6%

First sexual intercourse
Real number (ℝ)

Distinct21
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.057974387 × 1016
Minimum-2.481938517
Maximum5.31334694
Zeros0
Zeros (%)0.0%
Memory size5.2 KiB
2021-03-10T22:48:59.732605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-2.481938517
5-th percentile-1.064613888
Q1-0.7102827311
median-0.001620416877
Q30.3527107402
95-th percentile1.770035369
Maximum5.31334694
Range7.795285456
Interquartile range (IQR)1.062993471

Descriptive statistics

Standard deviation1.000763068
Coefficient of variation (CV)4.862854824 × 1015
Kurtosis4.601621467
Mean2.057974387 × 1016
Median Absolute Deviation (MAD)0.7086623142
Skewness1.677187924
Sum1.350031198 × 1013
Variance1.001526718
MonotocityNot monotonic
2021-03-10T22:48:59.794239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
-0.7102827311134
20.4%
-0.001620416877113
17.2%
0.3527107402113
17.2%
-0.35595157490
13.7%
-1.06461388859
9.0%
0.707041897345
 
6.9%
1.06137305427
 
4.1%
-1.41894504520
 
3.0%
1.41570421212
 
1.8%
3.1873599977
 
1.1%
Other values (11)36
 
5.5%
ValueCountFrequency (%)
-2.4819385171
 
0.2%
-2.127607362
 
0.3%
-1.7732762022
 
0.3%
-1.41894504520
 
3.0%
-1.06461388859
9.0%
ValueCountFrequency (%)
5.313346941
 
0.2%
4.2503534684
0.6%
3.8960223112
 
0.3%
3.5416911545
0.8%
3.1873599977
1.1%

Num of pregnancies
Real number (ℝ)

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.889597753 × 1017
Minimum-1.534870685
Maximum5.452465673
Zeros0
Zeros (%)0.0%
Memory size5.2 KiB
2021-03-10T22:48:59.855511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.534870685
5-th percentile-0.836137049
Q1-0.836137049
median-0.1374034131
Q30.5613302227
95-th percentile1.958797494
Maximum5.452465673
Range6.987336358
Interquartile range (IQR)1.397467272

Descriptive statistics

Standard deviation1.000763068
Coefficient of variation (CV)-1.699204444 × 1016
Kurtosis2.329712704
Mean-5.889597753 × 1017
Median Absolute Deviation (MAD)0.6987336358
Skewness1.374655096
Sum-3.863576126 × 1014
Variance1.001526718
MonotocityNot monotonic
2021-03-10T22:48:59.906137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
-0.836137049253
38.6%
-0.1374034131181
27.6%
0.5613302227104
15.9%
1.26006385956
 
8.5%
1.95879749428
 
4.3%
2.6575311316
 
2.4%
-1.53487068511
 
1.7%
3.3562647664
 
0.6%
4.0549984022
 
0.3%
5.4524656731
 
0.2%
ValueCountFrequency (%)
-1.53487068511
 
1.7%
-0.836137049253
38.6%
-0.1374034131181
27.6%
0.5613302227104
15.9%
1.26006385956
 
8.5%
ValueCountFrequency (%)
5.4524656731
 
0.2%
4.0549984022
 
0.3%
3.3562647664
 
0.6%
2.6575311316
2.4%
1.95879749428
4.3%

Smokes
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
558 
1.0
98 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0558
85.1%
1.098
 
14.9%
2021-03-10T22:49:00.023736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:00.062865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0558
85.1%
1.098
 
14.9%

Most occurring characters

ValueCountFrequency (%)
01214
61.7%
.656
33.3%
198
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01214
92.5%
198
 
7.5%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01214
61.7%
.656
33.3%
198
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01214
61.7%
.656
33.3%
198
 
5.0%

Smokes (years)
Real number (ℝ)

Distinct27
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.332577657 × 1017
Minimum-0.3073177302
Maximum8.52680928
Zeros0
Zeros (%)0.0%
Memory size5.2 KiB
2021-03-10T22:49:00.107139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.3073177302
5-th percentile-0.3073177302
Q1-0.3073177302
median-0.3073177302
Q3-0.3073177302
95-th percentile2.080284164
Maximum8.52680928
Range8.83412701
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.000763068
Coefficient of variation (CV)-2.309856041 × 1016
Kurtosis23.06458713
Mean-4.332577657 × 1017
Median Absolute Deviation (MAD)0
Skewness4.357923627
Sum-2.842170943 × 1014
Variance1.001526718
MonotocityNot monotonic
2021-03-10T22:49:00.169152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
-0.3073177302558
85.1%
-0.0048150383812
 
1.8%
1.8415239758
 
1.2%
-0.068557540717
 
1.1%
0.17020264876
 
0.9%
1.6027637856
 
0.9%
0.40896283825
 
0.8%
0.64772302765
 
0.8%
1.3640035965
 
0.8%
2.0802841645
 
0.8%
Other values (17)39
 
5.9%
ValueCountFrequency (%)
-0.3073177302558
85.1%
-0.26911609991
 
0.2%
-0.068557540717
 
1.1%
-0.0048150383812
 
1.8%
0.17020264876
 
0.9%
ValueCountFrequency (%)
8.526809281
0.2%
7.8105287111
0.2%
7.3330083321
0.2%
4.9454064382
0.3%
4.7066462481
0.2%

Smokes (packs/year)
Real number (ℝ)

Distinct55
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.415722071 × 1018
Minimum-0.2082177959
Maximum14.96874527
Zeros0
Zeros (%)0.0%
Memory size5.2 KiB
2021-03-10T22:49:00.240760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.2082177959
5-th percentile-0.2082177959
Q1-0.2082177959
median-0.2082177959
Q3-0.2082177959
95-th percentile0.9403091387
Maximum14.96874527
Range15.17696306
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.000763068
Coefficient of variation (CV)-1.847884833 × 1017
Kurtosis100.8218477
Mean-5.415722071 × 1018
Median Absolute Deviation (MAD)0
Skewness8.830350882
Sum-3.552713679 × 1015
Variance1.001526718
MonotocityNot monotonic
2021-03-10T22:49:00.320010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2082177959558
85.1%
0.00229165646213
 
2.0%
0.2019703955
 
0.8%
0.6121585864
 
0.6%
-0.12618015774
 
0.6%
1.0223467774
 
0.6%
-0.18770838633
 
0.5%
1.8427231593
 
0.5%
3.4834759222
 
0.3%
0.099423347322
 
0.3%
Other values (45)58
 
8.8%
ValueCountFrequency (%)
-0.2082177959558
85.1%
-0.20780760771
 
0.2%
-0.20698723131
 
0.2%
-0.19181026822
 
0.3%
-0.18770838633
 
0.5%
ValueCountFrequency (%)
14.968745271
0.2%
8.8159224051
0.2%
8.4057342141
0.2%
7.5853578321
0.2%
5.9446050681
0.2%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
1.0
451 
0.0
205 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
1.0451
68.8%
0.0205
31.2%
2021-03-10T22:49:00.457661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:00.497639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0451
68.8%
0.0205
31.2%

Most occurring characters

ValueCountFrequency (%)
0861
43.8%
.656
33.3%
1451
22.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
0861
65.6%
1451
34.4%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
0861
43.8%
.656
33.3%
1451
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
0861
43.8%
.656
33.3%
1451
22.9%
Distinct38
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.415722071 × 1017
Minimum-0.556973042
Maximum5.801523798
Zeros0
Zeros (%)0.0%
Memory size5.2 KiB
2021-03-10T22:49:00.544401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.556973042
5-th percentile-0.556973042
Q1-0.556973042
median-0.4847173961
Q30.02107212527
95-th percentile2.044230211
Maximum5.801523798
Range6.35849684
Interquartile range (IQR)0.5780451673

Descriptive statistics

Standard deviation1.000763068
Coefficient of variation (CV)1.847884833 × 1016
Kurtosis7.398807865
Mean5.415722071 × 1017
Median Absolute Deviation (MAD)0.07225564591
Skewness2.54101775
Sum3.552713679 × 1014
Variance1.001526718
MonotocityNot monotonic
2021-03-10T22:49:00.615606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
-0.556973042286
43.6%
-0.267950458460
 
9.1%
-0.484717396134
 
5.2%
0.0210721252730
 
4.6%
0.310094708927
 
4.1%
0.888139876227
 
4.1%
-0.412461750221
 
3.2%
1.1771624620
 
3.0%
-0.533851235317
 
2.6%
1.46618504315
 
2.3%
Other values (28)119
18.1%
ValueCountFrequency (%)
-0.556973042286
43.6%
-0.533851235317
 
2.6%
-0.510729428613
 
2.0%
-0.50783920281
 
0.2%
-0.484717396134
 
5.2%
ValueCountFrequency (%)
5.8015237981
 
0.2%
5.2234786313
0.5%
4.356410881
 
0.2%
4.0673882962
 
0.3%
3.7783657135
0.8%

IUD
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
595 
1.0
61 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0595
90.7%
1.061
 
9.3%
2021-03-10T22:49:00.746652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:00.786693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0595
90.7%
1.061
 
9.3%

Most occurring characters

ValueCountFrequency (%)
01251
63.6%
.656
33.3%
161
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01251
95.4%
161
 
4.6%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01251
63.6%
.656
33.3%
161
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01251
63.6%
.656
33.3%
161
 
3.1%

IUD (years)
Real number (ℝ)

Distinct24
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.895502725 × 1017
Minimum-0.2364019406
Maximum9.766700273
Zeros0
Zeros (%)0.0%
Memory size5.2 KiB
2021-03-10T22:49:00.829838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.2364019406
5-th percentile-0.2364019406
Q1-0.2364019406
median-0.2364019406
Q3-0.2364019406
95-th percentile1.343035251
Maximum9.766700273
Range10.00310221
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.000763068
Coefficient of variation (CV)-5.279670952 × 1016
Kurtosis36.59920905
Mean-1.895502725 × 1017
Median Absolute Deviation (MAD)0
Skewness5.588541463
Sum-1.243449788 × 1014
Variance1.001526718
MonotocityNot monotonic
2021-03-10T22:49:00.895349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
-0.2364019406595
90.7%
1.3430352519
 
1.4%
0.81655618716
 
0.9%
0.29007712336
 
0.9%
2.3959933796
 
0.9%
3.4489515075
 
0.8%
3.9754305714
 
0.6%
1.8695143154
 
0.6%
5.5548677623
 
0.5%
2.9224724433
 
0.5%
Other values (14)15
 
2.3%
ValueCountFrequency (%)
-0.2364019406595
90.7%
-0.19428361551
 
0.2%
-0.15216529041
 
0.2%
-0.14690049981
 
0.2%
-0.10478217471
 
0.2%
ValueCountFrequency (%)
9.7667002731
 
0.2%
8.7137421461
 
0.2%
7.6607840181
 
0.2%
6.0813468261
 
0.2%
5.5548677623
0.5%

STDs
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
597 
1.0
 
59

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0597
91.0%
1.059
 
9.0%
2021-03-10T22:49:01.018095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:01.057143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0597
91.0%
1.059
 
9.0%

Most occurring characters

ValueCountFrequency (%)
01253
63.7%
.656
33.3%
159
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01253
95.5%
159
 
4.5%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01253
63.7%
.656
33.3%
159
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01253
63.7%
.656
33.3%
159
 
3.0%

STDs (number)
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
-0.2875127911096561
597 
1.6771579481396606
 
28
3.6418286873889776
 
26
5.606499426638294
 
4
7.571170165887611
 
1

Length

Max length19
Median length19
Mean length18.90243902
Min length17

Characters and Unicode

Total characters12400
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row-0.2875127911096561
2nd row1.6771579481396606
3rd row-0.2875127911096561
4th row-0.2875127911096561
5th row-0.2875127911096561
ValueCountFrequency (%)
-0.2875127911096561597
91.0%
1.677157948139660628
 
4.3%
3.641828687388977626
 
4.0%
5.6064994266382944
 
0.6%
7.5711701658876111
 
0.2%
2021-03-10T22:49:01.174060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:01.223087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.2875127911096561597
91.0%
1.677157948139660628
 
4.3%
3.641828687388977626
 
4.0%
5.6064994266382944
 
0.6%
7.5711701658876111
 
0.2%

Most occurring characters

ValueCountFrequency (%)
12503
20.2%
61402
11.3%
71360
11.0%
91288
10.4%
21228
9.9%
51228
9.9%
01227
9.9%
8761
 
6.1%
.656
 
5.3%
-597
 
4.8%
Other values (2)150
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11147
89.9%
Other Punctuation656
 
5.3%
Dash Punctuation597
 
4.8%

Most frequent character per category

ValueCountFrequency (%)
12503
22.5%
61402
12.6%
71360
12.2%
91288
11.6%
21228
11.0%
51228
11.0%
01227
11.0%
8761
 
6.8%
384
 
0.8%
466
 
0.6%
ValueCountFrequency (%)
-597
100.0%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12400
100.0%

Most frequent character per script

ValueCountFrequency (%)
12503
20.2%
61402
11.3%
71360
11.0%
91288
10.4%
21228
9.9%
51228
9.9%
01227
9.9%
8761
 
6.1%
.656
 
5.3%
-597
 
4.8%
Other values (2)150
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII12400
100.0%

Most frequent character per block

ValueCountFrequency (%)
12503
20.2%
61402
11.3%
71360
11.0%
91288
10.4%
21228
9.9%
51228
9.9%
01227
9.9%
8761
 
6.1%
.656
 
5.3%
-597
 
4.8%
Other values (2)150
 
1.2%

STDs:condylomatosis
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
626 
1.0
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0626
95.4%
1.030
 
4.6%
2021-03-10T22:49:01.340921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:01.381933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0626
95.4%
1.030
 
4.6%

Most occurring characters

ValueCountFrequency (%)
01282
65.1%
.656
33.3%
130
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01282
97.7%
130
 
2.3%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01282
65.1%
.656
33.3%
130
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01282
65.1%
.656
33.3%
130
 
1.5%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
654 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0654
99.7%
1.02
 
0.3%
2021-03-10T22:49:01.487184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:01.526332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0654
99.7%
1.02
 
0.3%

Most occurring characters

ValueCountFrequency (%)
01310
66.6%
.656
33.3%
12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01310
99.8%
12
 
0.2%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01310
66.6%
.656
33.3%
12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01310
66.6%
.656
33.3%
12
 
0.1%

STDs:vulvo-perineal condylomatosis
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
626 
1.0
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0626
95.4%
1.030
 
4.6%
2021-03-10T22:49:01.627795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:01.666993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0626
95.4%
1.030
 
4.6%

Most occurring characters

ValueCountFrequency (%)
01282
65.1%
.656
33.3%
130
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01282
97.7%
130
 
2.3%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01282
65.1%
.656
33.3%
130
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01282
65.1%
.656
33.3%
130
 
1.5%

STDs:syphilis
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
643 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0643
98.0%
1.013
 
2.0%
2021-03-10T22:49:01.903924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:01.942042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0643
98.0%
1.013
 
2.0%

Most occurring characters

ValueCountFrequency (%)
01299
66.0%
.656
33.3%
113
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01299
99.0%
113
 
1.0%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01299
66.0%
.656
33.3%
113
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01299
66.0%
.656
33.3%
113
 
0.7%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
655 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0655
99.8%
1.01
 
0.2%
2021-03-10T22:49:02.044952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:02.083830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0655
99.8%
1.01
 
0.2%

Most occurring characters

ValueCountFrequency (%)
01311
66.6%
.656
33.3%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01311
99.9%
11
 
0.1%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01311
66.6%
.656
33.3%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01311
66.6%
.656
33.3%
11
 
0.1%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
655 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0655
99.8%
1.01
 
0.2%
2021-03-10T22:49:02.187900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:02.227811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0655
99.8%
1.01
 
0.2%

Most occurring characters

ValueCountFrequency (%)
01311
66.6%
.656
33.3%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01311
99.9%
11
 
0.1%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01311
66.6%
.656
33.3%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01311
66.6%
.656
33.3%
11
 
0.1%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
655 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0655
99.8%
1.01
 
0.2%
2021-03-10T22:49:02.332466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:02.371554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0655
99.8%
1.01
 
0.2%

Most occurring characters

ValueCountFrequency (%)
01311
66.6%
.656
33.3%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01311
99.9%
11
 
0.1%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01311
66.6%
.656
33.3%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01311
66.6%
.656
33.3%
11
 
0.1%

STDs:HIV
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
641 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0641
97.7%
1.015
 
2.3%
2021-03-10T22:49:02.476349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:02.515520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0641
97.7%
1.015
 
2.3%

Most occurring characters

ValueCountFrequency (%)
01297
65.9%
.656
33.3%
115
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01297
98.9%
115
 
1.1%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01297
65.9%
.656
33.3%
115
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01297
65.9%
.656
33.3%
115
 
0.8%

STDs:Hepatitis B
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
655 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0655
99.8%
1.01
 
0.2%
2021-03-10T22:49:02.626348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:02.670563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0655
99.8%
1.01
 
0.2%

Most occurring characters

ValueCountFrequency (%)
01311
66.6%
.656
33.3%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01311
99.9%
11
 
0.1%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01311
66.6%
.656
33.3%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01311
66.6%
.656
33.3%
11
 
0.1%

STDs:HPV
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
654 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0654
99.7%
1.02
 
0.3%
2021-03-10T22:49:02.788747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:02.833059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0654
99.7%
1.02
 
0.3%

Most occurring characters

ValueCountFrequency (%)
01310
66.6%
.656
33.3%
12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01310
99.8%
12
 
0.2%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01310
66.6%
.656
33.3%
12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01310
66.6%
.656
33.3%
12
 
0.1%

STDs: Number of diagnosis
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
602 
1.0
 
51
2.0
 
2
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0602
91.8%
1.051
 
7.8%
2.02
 
0.3%
3.01
 
0.2%
2021-03-10T22:49:02.954191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:02.999694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0602
91.8%
1.051
 
7.8%
2.02
 
0.3%
3.01
 
0.2%

Most occurring characters

ValueCountFrequency (%)
01258
63.9%
.656
33.3%
151
 
2.6%
22
 
0.1%
31
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01258
95.9%
151
 
3.9%
22
 
0.2%
31
 
0.1%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01258
63.9%
.656
33.3%
151
 
2.6%
22
 
0.1%
31
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01258
63.9%
.656
33.3%
151
 
2.6%
22
 
0.1%
31
 
0.1%

Dx:Cancer
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
639 
1.0
 
17

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0639
97.4%
1.017
 
2.6%
2021-03-10T22:49:03.128783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:03.168008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0639
97.4%
1.017
 
2.6%

Most occurring characters

ValueCountFrequency (%)
01295
65.8%
.656
33.3%
117
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01295
98.7%
117
 
1.3%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01295
65.8%
.656
33.3%
117
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01295
65.8%
.656
33.3%
117
 
0.9%

Dx:CIN
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
649 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0649
98.9%
1.07
 
1.1%
2021-03-10T22:49:03.275215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:03.314653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0649
98.9%
1.07
 
1.1%

Most occurring characters

ValueCountFrequency (%)
01305
66.3%
.656
33.3%
17
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01305
99.5%
17
 
0.5%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01305
66.3%
.656
33.3%
17
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01305
66.3%
.656
33.3%
17
 
0.4%

Dx:HPV
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
639 
1.0
 
17

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0639
97.4%
1.017
 
2.6%
2021-03-10T22:49:03.422580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:03.462116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0639
97.4%
1.017
 
2.6%

Most occurring characters

ValueCountFrequency (%)
01295
65.8%
.656
33.3%
117
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01295
98.7%
117
 
1.3%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01295
65.8%
.656
33.3%
117
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01295
65.8%
.656
33.3%
117
 
0.9%

Dx
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
635 
1.0
 
21

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0635
96.8%
1.021
 
3.2%
2021-03-10T22:49:03.567341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:03.606276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0635
96.8%
1.021
 
3.2%

Most occurring characters

ValueCountFrequency (%)
01291
65.6%
.656
33.3%
121
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01291
98.4%
121
 
1.6%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01291
65.6%
.656
33.3%
121
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01291
65.6%
.656
33.3%
121
 
1.1%

Hinselmann
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
626 
1.0
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0626
95.4%
1.030
 
4.6%
2021-03-10T22:49:03.708356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:03.747521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0626
95.4%
1.030
 
4.6%

Most occurring characters

ValueCountFrequency (%)
01282
65.1%
.656
33.3%
130
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01282
97.7%
130
 
2.3%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01282
65.1%
.656
33.3%
130
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01282
65.1%
.656
33.3%
130
 
1.5%

Schiller
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
599 
1.0
 
57

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0599
91.3%
1.057
 
8.7%
2021-03-10T22:49:03.849808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:03.889168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0599
91.3%
1.057
 
8.7%

Most occurring characters

ValueCountFrequency (%)
01255
63.8%
.656
33.3%
157
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01255
95.7%
157
 
4.3%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01255
63.8%
.656
33.3%
157
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01255
63.8%
.656
33.3%
157
 
2.9%

Citology
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0.0
624 
1.0
 
32

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0624
95.1%
1.032
 
4.9%
2021-03-10T22:49:03.991129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-10T22:49:04.032184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0624
95.1%
1.032
 
4.9%

Most occurring characters

ValueCountFrequency (%)
01280
65.0%
.656
33.3%
132
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1312
66.7%
Other Punctuation656
33.3%

Most frequent character per category

ValueCountFrequency (%)
01280
97.6%
132
 
2.4%
ValueCountFrequency (%)
.656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
100.0%

Most frequent character per script

ValueCountFrequency (%)
01280
65.0%
.656
33.3%
132
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1968
100.0%

Most frequent character per block

ValueCountFrequency (%)
01280
65.0%
.656
33.3%
132
 
1.6%

Interactions

2021-03-10T22:48:54.189991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:54.266820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:54.554184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:54.625791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:54.692418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:54.764930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:54.840470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:54.909249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:54.981303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:55.055584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:55.127523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:55.196098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:55.271546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:55.345440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:55.414726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:55.487642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:55.562065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:55.633419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:55.701673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:55.775105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:55.847419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:55.917249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:55.990682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:56.062780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:56.136106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:56.207762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:56.291949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:56.368191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:56.442137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:56.516699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:56.587607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:56.741422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:56.813587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:56.890836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:56.959406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:57.021243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:57.093131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:57.166322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:57.241080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:57.314664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:57.382171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:57.454344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:57.524174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:57.594214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:57.665411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:57.736922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:57.806389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:57.872632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:57.943285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:58.010677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:58.077216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:58.145354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:58.213630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:58.279707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:58.342543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-10T22:48:58.409838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-10T22:49:04.099864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-10T22:49:04.308937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-10T22:49:04.523026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-10T22:49:04.736406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-10T22:49:04.932442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-10T22:48:58.579678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-10T22:48:59.185946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

AgeNumber of sexual partnersFirst sexual intercourseNum of pregnanciesSmokesSmokes (years)Smokes (packs/year)Hormonal ContraceptivesHormonal Contraceptives (years)IUDIUD (years)STDsSTDs (number)STDs:condylomatosisSTDs:vaginal condylomatosisSTDs:vulvo-perineal condylomatosisSTDs:syphilisSTDs:pelvic inflammatory diseaseSTDs:genital herpesSTDs:molluscum contagiosumSTDs:HIVSTDs:Hepatitis BSTDs:HPVSTDs: Number of diagnosisDx:CancerDx:CINDx:HPVDxHinselmannSchillerCitology
0-1.531373-0.302480-1.064614-0.8361370.0-0.307318-0.2082180.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
10.2378781.427985-0.0016202.6575311.0-0.0048150.3250271.0-0.4124620.0-0.2364021.01.6771580.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.0
2-0.941623-0.302480-0.710283-0.1374030.0-0.307318-0.2082181.0-0.4124620.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
3-0.823672-0.302480-0.710283-0.8361370.0-0.307318-0.2082180.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
42.0071290.2743420.707042-0.8361370.0-0.307318-0.2082180.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
50.9455780.2743420.3527111.2600641.03.9903663.4834760.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
60.7096780.2743420.352711-0.1374030.0-0.307318-0.2082180.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.01.01.0
7-0.9416230.851163-1.064614-0.8361370.0-0.307318-0.2082181.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
8-0.233922-0.302480-0.355952-0.1374031.01.602764-0.1425881.00.3100950.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
9-0.705722-0.879301-0.001620-0.1374030.0-0.307318-0.2082181.00.3100950.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.0

Last rows

AgeNumber of sexual partnersFirst sexual intercourseNum of pregnanciesSmokesSmokes (years)Smokes (packs/year)Hormonal ContraceptivesHormonal Contraceptives (years)IUDIUD (years)STDsSTDs (number)STDs:condylomatosisSTDs:vaginal condylomatosisSTDs:vulvo-perineal condylomatosisSTDs:syphilisSTDs:pelvic inflammatory diseaseSTDs:genital herpesSTDs:molluscum contagiosumSTDs:HIVSTDs:Hepatitis BSTDs:HPVSTDs: Number of diagnosisDx:CancerDx:CINDx:HPVDxHinselmannSchillerCitology
646-0.8236720.851163-0.001620-0.8361370.0-0.307318-0.2082181.0-0.5107290.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
647-0.2339220.2743420.3527110.5613300.0-0.307318-0.2082180.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
648-0.8236720.2743420.352711-0.8361370.0-0.307318-0.2082181.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
649-1.177523-0.879301-1.064614-0.8361370.0-0.307318-0.2082181.00.0210720.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
650-0.8236720.274342-1.0646140.5613301.00.647723-0.1261800.0-0.5569730.0-0.2364021.01.6771580.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.0
6512.0071290.274342-0.710283-0.8361371.0-0.0048150.9403090.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
652-0.941623-0.8793010.707042-0.8361370.0-0.307318-0.2082180.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
6530.3558280.274342-0.710283-1.5348711.03.5128453.0732880.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
654-0.587772-0.879301-0.001620-0.1374030.0-0.307318-0.2082181.0-0.5107291.01.3430350.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
6550.355828-0.302480-1.4189450.5613301.04.9454061.1454030.0-0.5569730.0-0.2364021.01.6771580.00.00.00.00.00.00.01.00.00.01.00.00.00.00.01.01.00.0

Duplicate rows

Most frequent

AgeNumber of sexual partnersFirst sexual intercourseNum of pregnanciesSmokesSmokes (years)Smokes (packs/year)Hormonal ContraceptivesHormonal Contraceptives (years)IUDIUD (years)STDsSTDs (number)STDs:condylomatosisSTDs:vaginal condylomatosisSTDs:vulvo-perineal condylomatosisSTDs:syphilisSTDs:pelvic inflammatory diseaseSTDs:genital herpesSTDs:molluscum contagiosumSTDs:HIVSTDs:Hepatitis BSTDs:HPVSTDs: Number of diagnosisDx:CancerDx:CINDx:HPVDxHinselmannSchillerCitologycount
0-1.413423-0.879301-1.064614-0.8361370.0-0.307318-0.2082180.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.04
2-1.295473-0.879301-0.710283-0.8361370.0-0.307318-0.2082180.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.03
1-1.413423-0.302480-1.064614-0.8361370.0-0.307318-0.2082180.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02
3-1.295473-0.879301-0.355952-0.8361370.0-0.307318-0.2082180.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02
4-1.177523-0.879301-0.001620-0.8361370.0-0.307318-0.2082180.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02
5-1.177523-0.302480-0.710283-0.8361370.0-0.307318-0.2082181.0-0.4615960.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02
6-1.059573-0.879301-1.064614-0.1374030.0-0.307318-0.2082180.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02
7-1.059573-0.879301-0.001620-0.8361370.0-0.307318-0.2082180.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02
8-1.059573-0.8793010.352711-0.8361370.0-0.307318-0.2082180.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02
9-0.9416230.851163-1.064614-0.8361370.0-0.307318-0.2082181.0-0.5569730.0-0.2364020.0-0.2875130.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02